Instrumented crutches for gait parameters evaluation

Most of the prototypes of instrumented crutches available in the literature require external motion capture devices to perform a gait analysis and to report the load applied on the crutches with respect to the gait cycle. Motion capture systems with markers require a controlled laboratory with cameras, instead IMU-based systems are more transportable, but the user must be instrumented. A new version of instrumented crutches, previously developed by the authors, allows one to measure the axial forces and to detect the gait phases during two-point assisted walking thanks to the cameras mounted on the lower part of the crutches

Leading the Way: Catholic School Leaders and Action Research

Recent research extols the value of problem-based learning strategies in exemplary school leadership preparation programs as one way to provide school leaders with the appropriate tools to systematically use data to make important decisions. The purpose of this study was to address the current gap between the noted importance of problem-based learning strategies in leadership preparation programs, and the demonstrated effect these strategies have on the knowledge, skills, behaviours, and values of school leaders. The study employed a longitudinal mixed-method research design to examine discrete action research skills, behaviours, and values of 44 candidates enrolled in a Master of Arts in Educational Administration degree program. Inferential analysis of the pre- and post-test survey data indicated a statistically significant increase in self-reported preparedness and capacity for all but two of the 14 core research activities assessed on the survey instrument. There are powerful and potentially long lasting outcomes for leadership candidates that complete a full cycle of action research as part of a principal preparation program. This study allows some tentative mapping of the actual skills, behaviours, and values that school leaders may evince as a result of deep exposure to practitioner driven action research

Body measurement estimations using 3D scanner for individuals with severe motor impairments

In biomechanics, a still unresolved question is how to estimate with enough accuracy the volume and mass of each body segment of a subject. This is important for several applications ranging from the rehabilitation of injured subjects to the study of athletic performances via the analysis of the dynamic inertia of each body segment. However, traditionally this evaluation is done by referring to anthropometric tables or by approximating the volumes using manual measurements. We propose a novel method based on the 3D reconstruction of the subject’s body using the commercial low-cost camera Kinect v2. The software developed performs body segment separation in a few minutes leveraging alpha shape approximation of 3D polyhedrons to quickly compute a Montecarlo volume estimation. The procedure was evaluated on a total of 30 healthy subjects and the resulting segments’ lengths and masses were compared with the literature

Experimental Procedure for the Metrological Characterization of Time-of-Flight Cameras for Human Body 3D Measurements

Time-of-flight cameras are widely adopted in a variety of indoor applications ranging from industrial object measurement to human activity recognition. However, the available products may differ in terms of the quality of the acquired point cloud, and the datasheet provided by the constructors may not be enough to guide researchers in the choice of the perfect device for their application. Hence, this work details the experimental procedure to assess time-of-flight cameras' error sources that should be considered when designing an application involving time-of-flight technology, such as the bias correction and the temperature influence on the point cloud stability. This is the first step towards a standardization of the metrological characterization procedure that could ensure the robustness and comparability of the results among tests and different devices. The procedure was conducted on Kinect Azure, Basler Blaze 101, and Basler ToF 640 cameras. Moreover, we compared the devices in the task of 3D reconstruction following a procedure involving the measure of both an object and a human upper-body-shaped mannequin. The experiment highlighted that, despite the results of the previously conducted metrological characterization, some devices showed evident difficulties in reconstructing the target objects. Thus, we proved that performing a rigorous evaluation procedure similar to the one proposed in this paper is always necessary when choosing the right device

Misura dell'orientamento di pezzi meccanici a geometria variabile tramite Machine Learning - sviluppo algoritmi e validazione metrologica

L’identificazione corretta della posizione e dell’orientamento di pezzi meccanici a geometria variabile è uno dei maggiori problemi nelle applicazioni di pick & place in ambito industriale. Riuscire a identificare correttamente il modo in cui il pezzo oggetto della misura è posizionato in modo da riuscire a prenderlo e spostarlo risulta fondamentale nei processi industriali automatici in cui sono presenti numerose celle robotiche tra una macchina utensile e l’altra. Il problema viene spesso affrontato tramite tecniche basate su visione 2D che, però, presentano dei limiti quando i pezzi meccanici da prelevare possiedono una geometria tale da uscire dal dominio bidimensionale. Parallelamente, l’approccio 3D presenta una problematica legata soprattutto alla geometria variabile, che non consente lo sviluppo di un algoritmo robusto per l’identificazione del posizionamento del pezzo. Per superare queste limitazioni, negli ultimi anni sono state sviluppate tecniche di misura basate su machine learning che consentono di arginare i problemi legati alla variabilità della geometria. La presente memoria descrive lo sviluppo di un algoritmo di misura della posizione e dell’orientamento di pezzi meccanici di geometria variabile. I pezzi meccanici considerati sono stati ricavati da operazioni di stampaggio e presentano bave sul contorno che rendono gli approcci standard inefficaci e poco accurati nella misura. Per questo motivo, è stato sviluppato un algoritmo di misura che sfrutta una combinazione di tecniche di machine learning e tecniche classiche di visione 3D che permette di ottenere la matrice di rototraslazione dei pezzi oggetti della misura rispetto al relativo modello CAD di progettazione. Grazie alla matrice di rototraslazione ottenuta, è possibile fornire al robot la posizione accurata di alcuni punti scelti manualmente e utilizzati dal robot stesso per effettuare la presa del pezzo. L’algoritmo sviluppato opera su una nuvola di punti 3D del pezzo meccanico comprensivo di bave. Una volta effettuata la scansione sono previste diverse fasi: (i) ritaglio automatico della nuvola in modo da ricavarne solamente il pezzo in esame, (ii) rimozione automatica delle blob di punti identificate come outlier rispetto alla nuvola del pezzo, (iii) identificazione della posa del pezzo meccanico tramite classificatore basato su machine learning, (iv) allineamento grossolano tra pezzo meccanico (SCAN) e il relativo modello di riferimento (RIF) tramite analisi PCA (Principal Component Analysis) e (v) allineamento fine tra pezzo meccanico e modello CAD tramite algoritmo ICP (Iterative Closest Point)

Catholic Education: A Journal of Inquiry & Practice: A Ten-Year Retrospective Review of Catholic Educational Research

The journal has a brief but important history, encompassing the support of major Catholic colleges and universities across the United States. In particular, the University of Dayton and the University of Notre Dame have provided a home for the editorial offices and the contributed services of the editors. As the journal prepares for a transition to its third home at Boston College, this article offers a summative and evaluative overview of the contents of the journal since its inception. Recommendations are offered regarding ways to continue to grow the field of educational research situated in Catholic schools

Validation of a smart mirror for gesture recognition in gym training performed by a vision-based deep learning system

This paper illustrates the development and validation of a smart mirror for sports training. The application is based on the skeletonization algorithm MediaPipe and runs on an embedded device Nvidia Jetson Nano equipped with two fisheye cameras. The software has been evaluated considering the exercise biceps curl. The elbow angle has been measured by both MediaPipe and the motion capture system BTS (ground truth), and the resulting values have been compared to determine angle uncertainty, residual errors, and intra-subject and inter-subject repeatability. The uncertainty of the joints’ estimation and the quality of the image captured by the cameras reflect on the final uncertainty of the indicator over time, highlighting the areas of improvement for further development

Pastors’ Views of Parents and the Parental Role in Catholic Schools

Over 300 years of official Church teachings and documents affirm the importance of the home-school relationship, yet relatively little research has systematically explored the need and value of parent involvement in the school community. This study is a secondary analysis of survey data collected for the Notre Dame Study of U.S. Pastors (Nuzzi, Frabutt, & Holter, 2008) and examines pastors’ views of parents and the parental role in Catholic schools. The article closes with recommendations for action based upon analysis of the quantitative and qualitative data trends from pastors’ responses

Validazione di algoritmi di calibrazione estrinseca basati su skeletonization del corpo umano

La presente memoria descrive le procedure utilizzate per la valutazione metrologica di procedure di calibrazione estrinseca di sistemi di visione composti da più telecamere. Viene definita calibrazione estrinseca quella procedura che consente di calcolare posizione ed orientamento di ogni telecamera presente in un sistema multicamera rispetto a tutte le altre. I metodi di calibrazione estrinseca si possono dividere principalmente in tre gruppi: tradizionali, basati sul riconoscimento di forme tridimensionali e basati su skeletonization. I metodi di calibrazione tradizionali si basano sull’utilizzo di target di calibrazione noti (scacchiere, griglie di punti, frange, etc) che vengono riconosciuti automaticamente dal sistema. Il sistema misura la posizione dei punti caratteristici del target ottenendo in questo modo i parametri di rotazione e traslazione desiderati. I metodi basati sul riconoscimento di forme tridimensionali (3D shape matching) sono invece fondati sulla coerenza geometrica di un oggetto 3D posizionato nel campo di vista delle varie telecamere: ciascun dispositivo registra una parte dell’oggetto target e successivamente, allineando ciascuna vista con le rimanenti, ed analizzando la traiettoria dell’oggetto vista da ogni telecamera è possibile risalire alle matrici di calibrazione. I metodi di calibrazione tradizionali, così come quelli basati su 3D shape matching risultano svantaggiosi in termini di tempo di esecuzione. Inoltre, queste tipologie necessitano di un target di calibrazione. Infine, i metodi basati sul riconoscimento dello scheletro umano (skeleton-based) utilizzano come target di calibrazione direttamente le articolazioni (joint) di un operatore che si posiziona all’interno del campo di vista delle telecamere. I metodi skeleton-based rappresentano quindi un’evoluzione dei metodi di 3D shape matching in quanto è come se venissero considerate forme 3D multiple rappresentate dai segmenti corporei dell’operatore stesso. Risulta quindi possibile ottenere una calibrazione estrinseca senza alcun oggetto caratteristico, ma semplicemente utilizzando il corpo dell’operatore umano come oggetto stesso. Nonostante in letteratura siano presenti lavori relativi alla valutazione dell’accuratezza nella misura dei joint, non sono presenti lavori che mostrano come questa accuratezza venga propagata a livello di matrici di rototraslazione risultanti dalla procedura di calibrazione. Il presente lavoro descrive le procedure utilizzate per valutare l’affidabilità della calibrazione estrinseca ottenuta tramite le posizioni dei joint misurate tramite il metodo di skeletonization descritto in [3]

Mesenchymal stem cell-derived extracellular vesicles protect human corneal endothelial cells from endoplasmic reticulum stress-mediated apoptosis

Corneal endothelial dystrophy is a relevant cause of vision loss and corneal transplantation worldwide. In the present study, we analyzed the effect of mesenchymal stem cell (MSC)-derived extracellular vesicles (MSC-EVs) in an in vitro model of corneal dystrophy, characterized by endoplasmic reticulum stress. The effects of MSC-EVs were compared with those of serum-derived EVs, reported to display a pro-angiogenic activity. MSC-EVs were able to induce a significant down-regulation of the large majority of endoplasmic reticulum stress-related genes in human corneal endothelial cells after exposure to serum deprivation and tunicamycin. In parallel, they upregulated the Akt pathway and limited caspase-3 activation and apoptosis. At variance, the effect of the serum EVs was mainly limited to Akt phosphorylation, with minimal or absent effects on endoplasmic reticulum stress modulation and apoptosis prevention. The effects of MSC-EVs were correlated to the transfer of numerous endoplasmic reticulum (ER)-stress targeting miRNAs to corneal endothelial cells. These data suggest a potential therapeutic effect of MSC-EVs for corneal endothelial endoplasmic reticulum stress, a major player in corneal endothelial dystrophy